Checking the censored two-sample accelerated life model using integrated cumulative hazard difference

In this dissertation, soma lack-of-fit tests will be discussed for the censored two-sample accelerated life model. Conventional scale estimators with two-sample censored data such as rank-based estimators and minimum distance estimators have difficulties to apply easily due to the fact that their asymptotic variances involve the unknown density, or they require soma strict conditions. The object of this work is to provide an asymptotically equivalent martingale-based stochastic process of some estimating functions, which is easier to apply than existing methods from the literature. An extreme value of the observed process compared with simulated realizations of the approximation process would indicate the model misspecifications. The approximation process involving the martingale structure can be achieved through some approximation procedures of the observed process under the two-sample scale model. The p-value applied to the approximation of the observed process leads to the construction of the lack-of-fit tests. Comparison of the processes enables one to get some information visually from the graph about how the model is misspecified.